In a typical cellular radio system, mobile terminals (also known as mobile stations and mobile user equipment units (UEs)) communicate via a radio access network (RAN) to one or more core networks. The user equipment units (UEs) can be mobile stations such as mobile telephones (“cellular” telephones) and laptops with mobile termination, and thus can be, for example, portable, pocket, hand-held, computer-included, or car-mounted mobile devices which communicate voice and/or data with the radio access network.
The radio access network (RAN) covers a geographical area which is divided into cell areas, with each cell area being served by a base station, e.g., a radio base station (RBS), which in some networks is also called “NodeB” or “B node”. A cell is a geographical area where radio coverage is provided by the radio base station equipment at a base station site. Each cell is identified by a unique identity within the local radio area, which is broadcast in the cell. The base stations communicate over the air interface (e.g., radio frequencies) with the user equipment units (UE) within range of the base stations. In the radio access network, several base stations are typically connected (e.g., by landlines or microwave) to a radio network controller (RNC). The radio network controller, also sometimes termed a base station controller (BSC), supervises and coordinates various activities of the plural base stations connected thereto. The radio network controllers are typically connected to one or more core networks.
The Universal Mobile Telecommunications System (UMTS) is a third generation or “3G” mobile communication system, which evolved from the Global System for Mobile Communications (GSM), and is intended to provide improved mobile communication services based on Wideband Code Division Multiple Access (WCDMA) technology. UTRAN is essentially a radio access network providing wideband code division multiple access for user equipment units (UEs). The Third Generation Partnership Project (3GPP) has undertaken to evolve further the predecessor technologies, e.g., GSM-based and/or second generation (“2G”) radio access network technologies.
With the adoption of release 6 of the WCDMA standard, a new type of uplink user was introduced. This new user was called an “enhanced” uplink (EUL) user. EUL users are characterized by short (10 or 2 ms) transmit time intervals (TTI), single or multi-code transmission, and potentially large transmit powers. These characteristics enable significantly higher peak data rates than what was achievable previously. Unfortunately, such high-power transmissions significantly increase interference for other uplink users.
One way to address the interference is through the use of a generalized RAKE (G-RAKE) receiver. G-RAKE receivers have been employed in WCDMA receivers to combat interference. The interference can be due to self-interference caused by multi-path propagation or other-user/cell interference caused by nearby/remote users (uplink) or other cells (downlink).
The goal of the G-RAKE receiver generally is to maximize the signal to interference plus noise ratio of the demodulated signal. To this end, a G-RAKE receiver typically includes a plurality of correlators, sometimes referred to as fingers, to separately despread different time-shifted signal images, and a combiner to combine the correlator outputs. A delay searcher processes the received signal to identify the delays corresponding to the strongest signal images (signal delays), and a finger placement processor determines the processing delays based on those signal delays. The process of finger placement comprises the allocation of a RAKE finger to each processing delay.
An example generalized RAKE (G-RAKE) receiver is described, e.g., in U.S. Pat. No. 6,363,104, which is incorporated herein by reference. The G-RAKE receiver was proposed to suppress interference in CDMA systems. See, e.g., G. Bottomley, T. Ottosson, Y.-P. B. Wang, “A Generalized RAKE Receiver for Interference Suppression”; IEEE Journal on Selected Areas of Communications, vol. 18, no. 8, pp. 1536-1545, August 2000. Interference suppression is achieved through a set of combining weights that account for correlation between the receiver fingers. The combining weights are given by Expression (1a)w=Ru−1h  Expression (1a)where w is a vector of combining weights, Ru is an impairment covariance matrix (including both interference and noise), and h is a vector of net channel coefficients (the term “net” refers to the combined contribution of the transmit filter, radio channel g, and receive filter). Parametric computation of an impairment covariance matrix is described, e.g., in U.S. patent application Ser. No. 11/219,183, entitled “ADAPTIVE TIMING RECOVERY VIA GENERALIZED RAKE RECEPTION”, and United States Patent Publication 2005/0201447/A1, both of which are incorporated herein by reference in their entirety. The impairment covariance matrix is also used to estimate signal quality, such as SINR, using for exampleSINR=hHRu−1h  Expression (1b)Determining the impairment covariance matrix is typically a prerequisite to generating a proper set of combining weights. It is also a computationally demanding step that must be performed for each uplink user. Thus, the complexity of the G-RAKE solution may exceed the available baseband computation resources as the cell load increases.
Particularly described below are three existing general approaches for computing an impairment covariance matrix (or the related data correlation matrix). A first such approach is a nonparametric impairment covariance matrix approach.
There are two ways to realize the nonparametric impairment covariance matrix. The first approach works at the symbol level. In this first approach, the pilot channel (or pilot symbols) over a slot are used to compute a measurement of the impairment covariance matrix. The slot-to-slot measured impairment covariance matrices are averaged to give an estimate of Ru. As an example, consider the following sequence of three steps. First, channel estimates are computed from despread pilot symbols in accordance with Expression (2).
                              h          ^                =                              1                          N              p                                ⁢                                    ∑                              k                =                0                                                              N                  p                                -                1                                      ⁢                                                            x                  pilot                                ⁡                                  (                  k                  )                                            ⁢                                                                    s                    *                                    ⁡                                      (                    k                    )                                                  .                                                                        Expression        ⁢                                  ⁢                  (          2          )                    Concerning Expression (2), xpilot(k) is a vector of despread pilot symbols across all fingers corresponding to the kth symbol in a slot, s(k) is the kth pilot symbol value, and Np is the number of pilot symbols in a slot. Then, a measurement of the impairment covariance matrix is computed using despread pilot symbols and the channel estimate in accordance with Expression (3).
                                          R            ^                    u          meas                =                              1                                          N                p                            -              1                                ⁢                                    ∑                              k                =                0                                                              N                  p                                -                1                                      ⁢                                          (                                                                                                    x                        pilot                                            ⁡                                              (                        k                        )                                                              ⁢                                                                  s                        *                                            ⁡                                              (                        k                        )                                                                              -                                      h                    ^                                                  )                            ⁢                                                                    (                                                                                                                        x                            pilot                                                    ⁡                                                      (                            k                            )                                                                          ⁢                                                                              s                            *                                                    ⁡                                                      (                            k                            )                                                                                              -                                              h                        ^                                                              )                                    H                                .                                                                        Expression        ⁢                                  ⁢                  (          3          )                    Finally, an estimate of the impairment covariance matrix is formed by averaging multiple measured impairment covariance matrices. A simple way of implementing this average is with an exponential filter such as that understood with reference to Expression (4).{circumflex over (R)}u(n)=λ{circumflex over (R)}u(n−1)+(1−λ){circumflex over (R)}umeas.  Expression (4)In Expression (4), λ is a filter parameter (e.g., forgetting factor) which determines how fast a previous estimate is phased out, and usually is a real value constant with a value between zero and one.
The second approach for the nonparametric impairment covariance matrix works at the chip level. The idea of the second approach is to form outer products of chip-level data for all delays of interest in accordance with Expression (5).
                                          R            ^                    d                =                              1                          (                              2560                C                            )                                ⁢                                    ∑                              m                =                0                                                              (                                      2560                    C                                    )                                -                1                                      ⁢                                          y                ⁡                                  (                  mC                  )                                            ⁢                                                                    y                    H                                    ⁡                                      (                    mC                    )                                                  .                                                                        Expression        ⁢                                  ⁢                  (          5          )                    In Expression (5), y(n) is a vector of chips corresponding to the desired delays for chip n in the current slot, C is the update rate, and (as in other expressions) the superscript H stands for Hermitian transpose. The update rate controls the complexity and performance of this approach. One can then use {circumflex over (R)}d in place of Ru when computing combining weights provided that the soft bit information is properly adjusted prior to decoding, or an estimate of the impairment covariance matrix can be computed from Expression (6).{circumflex over (R)}u={circumflex over (R)}d−ĥĥH.  Expression (6)
A second approach for computing an impairment covariance matrix (or the related data correlation matrix) is the parametric impairment covariance matrix approach. Using the parametric impairment covariance matrix approach, a model is employed to construct the impairment covariance matrix. In the most general form, this model is given by Expression (7).
                              R          u                =                                                            E                c                            ⁡                              (                0                )                                      ⁢                                          R                0                                                                                          ⁢                  own                                            ⁡                              (                                  g                  0                                )                                              +                                    ∑                              j                =                1                            J                        ⁢                                                            E                  c                                ⁡                                  (                  j                  )                                            ⁢                                                R                  j                  own                                ⁡                                  (                                      g                    j                                    )                                                              +                                    N              0                        ⁢                                          R                n                            .                                                          Expression        ⁢                                  ⁢                  (          7          )                    In Expression (8), gj is a vector of medium channel coefficients corresponding to the channel between the jth uplink user and the base station, Ec(i) represents the total energy per chip of uplink user j, and N0 represents the power of the white noise passing through the receive filter. The assumption in Expression (7) is that user 0 is the one to be demodulated, while the matrix terms corresponding to users 1 through J represent interference. Therefore, R0own(g0) is a self-interference term. The entries of this matrix are given by Expression (8)
                                          R            0                                                                      ⁢              own                                ⁡                      (                                          d                1                            ,                              d                2                                      )                          =                              ∑                          l              =              0                                                      L                j                            -              1                                ⁢                                    ∑                              q                =                0                                                              L                  j                                -                1                                      ⁢                                                            g                  0                                ⁡                                  (                  l                  )                                            ⁢                                                g                  0                  *                                ⁡                                  (                  q                  )                                            ⁢                                                ∑                                                            m                      =                                              -                        ∞                                                                                    m                      ≠                      0                                                                            m                    =                                          +                      ∞                                                                      ⁢                                                                            R                                              Tx                        /                        Rx                                                              ⁡                                          (                                                                        d                          1                                                -                                                  mT                          c                                                -                                                                              τ                            0                                                    ⁡                                                      (                            l                            )                                                                                              )                                                        ⁢                                                                                    R                                                  Tx                          /                          Rx                                                *                                            ⁡                                              (                                                                              d                            2                                                    -                                                      mT                            c                                                    -                                                                                    τ                              0                                                        ⁡                                                          (                              q                              )                                                                                                      )                                                              .                                                                                                          Exp        .                                  ⁢                  (          8          )                    The remaining terms represent other-user interference. The entries of these matrices are given by Expression (9)
                                          R            j                                                                      ⁢              other                                ⁡                      (                                          d                1                            ,                              d                2                                      )                          =                              ∑                          l              =              0                                                      L                j                            -              1                                ⁢                                    ∑                              q                =                0                                                              L                  j                                -                1                                      ⁢                                                            g                  j                                ⁡                                  (                  l                  )                                            ⁢                                                g                  j                  *                                ⁡                                  (                  q                  )                                            ⁢                                                ∑                                      m                    =                                          -                      ∞                                                                            m                    =                                          +                      ∞                                                                      ⁢                                                                            R                                              Tx                        /                        Rx                                                              ⁡                                          (                                                                        d                          1                                                -                                                  mT                          c                                                -                                                                              τ                            j                                                    ⁡                                                      (                            l                            )                                                                                              )                                                        ⁢                                                            R                                              Tx                        /                        Rx                                            *                                        ⁡                                          (                                                                        d                          2                                                -                                                  mT                          c                                                -                                                                              τ                            j                                                    ⁡                                                      (                            q                            )                                                                                              )                                                                                                                              Expression        ⁢                                  ⁢                  (          9          )                    In Expression (8) and Expression (9), RTx/Rx (Δ) represents the convolution of the transmit and receive filters evaluated at Δ, Tc is the chip duration, and gj(q) and τj(q) are the complex coefficient and path delay for the qth path of user j signal, respectively, and d1 and d2 are finger delays. Note the similarity of Expression (8) and Expression (9): only the m=0 term differentiates the two expressions.
A third approach for computing an impairment covariance matrix (or the related data correlation matrix) is the data correlation matrix approach. With {circumflex over (R)}u, G-Rake combining weights can be formulated as w={circumflex over (R)}u−1ĥ. Another approach is to use data covariance {circumflex over (R)}d in computing the combining weights, w′={circumflex over (R)}d−1ĥ. Similar to the proof in H. Hadinejad-Mahram, “On the equivalence of linear MMSE chip-level equalizer and generalized RAKE”, IEEE Commun. Lett., Vol. 8, pp. 7-8, January 2004, by using the matrix inversion lemma it can be shown that w=αw′, where α is a positive scalar. Exploiting the relationship between w and w′ and realizing the multiple G-Rake receivers in the uplink share the same receive samples, it has been proposed to use {circumflex over (R)}d in G-Rake combining weight formulation. See, in this regard, G. E. Bottomley, C. Cozzo, H. Eriksson, A. S. Khayrallah, Y.-P. E. Wang, “Reduced Complexity Interference Suppression for Wireless Communications”, U.S. patent application Ser. No. 11/276,069 (incorporated herein by reference), according to which {circumflex over (R)}d can be estimated for a grid (or a set) of delays that cover all the finger delays among different uplink receivers. Then, {circumflex over (R)}d or {circumflex over (R)}d−1 can be shared by G-Rake receivers intended for different users.
Each of the three above-described approaches have shortcomings, as explained below.
Since there are two ways to realize the nonparametric impairment covariance matrix approach, each is considered separately. The major limitation of the nonparametric approach at the symbol level is the number of pilot symbols available to form measured impairment covariance matrices. Typically there are 10 pilots or less per slot. This means that the measured impairment covariance matrix is quite noisy, so significant averaging is required to get good demodulation performance. The required averaging limits the applicability of this approach to very low mobile speeds.
The limitation of the nonparametric form at the chip level is that one must trade performance for complexity. Simulations have shown that to achieve performance almost equivalent to the parametric form, computations in the chip-level nonparametric approach must be performed at the chip rate. For some performance loss, the chip-level nonparametric form may be computed at a reduced rate. While it has been estimated that the complexity of the chip-level variant is reasonable for a computation rate of C=16, some additional performance loss is incurred.
The limitations discussed above for the chip-level nonparametric impairment covariance approach apply to the data correlation matrix and to methods proposed in G. E. Bottomley, C. Cozzo, H. Eriksson, A. S. Khayrallah, Y.-P. E. Wang, “Reduced Complexity Interference Suppression for Wireless Communications”, U.S. patent application Ser. No. 11/276,069, 2006 Feb. 13 since they all require estimating data covariance matrices based on chip samples.
The major limitation of the parametric impairment covariance matrix approach is the complexity. The complexity is directly proportional to the number of uplink users. Also, for each uplink user, the complexity is a function of the square of the number of paths and the square of the number of fingers. Thus the overall complexity can become prohibitive as the number of uplink users increases. Of course one could ignore all terms aside from self-interference (for example) to reduce complexity. This approach has been taken in the downlink. However, in the EUL scenario, there could be a large interferer that is not suppressed which would cause serious performance degradation.
What is needed, and an object of the present invention, are one or more of method, apparatus and technique to reduce the complexity of computing the impairment covariance matrix for each uplink user.